Multi-agent Decision-making at Unsignalized Intersections with Reinforcement Learning from Demonstrations

C Huang, J Zhao, H Zhou, H Zhang… - 2023 IEEE Intelligent …, 2023 - ieeexplore.ieee.org
Intersections are key nodes and also bottlenecks of urban road networks, so improving the
traffic efficiency at intersections is beneficial to improving overall traffic throughput and …

Cooperative Decision-Making for CAVs at Unsignalized Intersections: A MARL Approach with Attention and Hierarchical Game Priors

J Liu, P Hang, X Na, C Huang, J Sun - Authorea Preprints, 2023 - techrxiv.org
The development of autonomous vehicles has shown great potential to enhance the
efficiency and safety of transportation systems. However, the decision-making issue in …

Hierarchical reinforcement learning for dynamic autonomous vehicle navigation at intelligent intersections

Q Sun, L Zhang, H Yu, W Zhang, Y Mei… - Proceedings of the 29th …, 2023 - dl.acm.org
Recent years have witnessed the rapid development of the Cooperative Vehicle
Infrastructure System (CVIS), where road infrastructures such as traffic lights (TL) and …

Parameter sharing reinforcement learning for modeling multi-agent driving behavior in roundabout scenarios

F Konstantinidis, M Sackmann… - 2021 IEEE …, 2021 - ieeexplore.ieee.org
Modeling other drivers' behavior in highly interactive traffic situations, such as roundabouts,
is a challenging task. We address this task using a Multi-Agent Reinforcement Learning …

Learning from Oracle demonstrations—a new approach to develop autonomous intersection management control algorithms based on multiagent deep reinforcement …

A Guillen-Perez, MD Cano - IEEE Access, 2022 - ieeexplore.ieee.org
Worldwide, many companies are working towards safe and innovative control systems for
Autonomous Vehicles (AVs). A key component is Autonomous Intersection Management …

Coordination for connected and automated vehicles at non-signalized intersections: A value decomposition-based multiagent deep reinforcement learning approach

Z Guo, Y Wu, L Wang, J Zhang - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
The recent proliferation of the research on multi-agent deep reinforcement learning (MDRL)
offers an encouraging way to coordinate multiple connected and automated vehicles (CAVs) …

Modeling Interaction-Aware Driving Behavior using Graph-Based Representations and Multi-Agent Reinforcement Learning

F Konstantinidis, M Sackmann… - 2023 IEEE 26th …, 2023 - ieeexplore.ieee.org
Modeling the driving behavior of traffic partici-pants in highly interactive traffic situations,
such as roundabouts, poses a significant challenge due to the complex interactions and the …

Real-time intelligent autonomous intersection management using reinforcement learning

U Gunarathna, S Karunasekera… - 2022 IEEE Intelligent …, 2022 - ieeexplore.ieee.org
Autonomous intersection management has the ability to reduce congestion at intersections
significantly, compared to classical traffic signal control in the era of connected autonomous …

A Distributed Approach to Autonomous Intersection Management via Multi-Agent Reinforcement Learning

M Cederle, M Fabris, GA Susto - arXiv preprint arXiv:2405.08655, 2024 - arxiv.org
Autonomous intersection management (AIM) poses significant challenges due to the
intricate nature of real-world traffic scenarios and the need for a highly expensive centralised …

Intelligent autonomous intersection management

U Gunarathna, S Karunasekara… - arXiv preprint arXiv …, 2022 - arxiv.org
Connected Autonomous Vehicles will make autonomous intersection management a reality
replacing traditional traffic signal control. Autonomous intersection management requires …